diff --git a/CLAUDE.md b/CLAUDE.md index 3e0a652..b24ef75 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -50,7 +50,7 @@ python -m pytest tests/ **Key CLI flags for the simulation:** `--scenario` (no_notice|alert_guided|advice_guided), `--messaging` (on|off), `--events` (on|off), `--web-dashboard` (on|off), `--metrics` (on|off), `--overlays` (on|off). -**Key environment variables:** `OPENAI_MODEL` (default: `gpt-4o-mini`), `DECISION_PERIOD_S` (default: `5.0`), `NET_FILE` (default: `sumo/Repaired.rou.xml`), `SUMO_CFG` (default: `sumo/Repaired.sumocfg`), `RUN_MODE`, `REPLAY_LOG_PATH`, `EVENTS_LOG_PATH`, `METRICS_LOG_PATH`. +**Key environment variables:** `OPENAI_MODEL` (default: `gpt-4o-mini`), `DECISION_PERIOD_S` (default: `5.0`), `NET_FILE` (default: `sumo/Repaired.net.xml`), `SUMO_CFG` (default: `sumo/Repaired.sumocfg`), `RUN_MODE`, `REPLAY_LOG_PATH`, `EVENTS_LOG_PATH`, `METRICS_LOG_PATH`. ## Architecture @@ -80,7 +80,7 @@ python -m pytest tests/ At the top of the file (labeled `USER CONFIG`): - `CONTROL_MODE` — `"destination"` (default) or `"route"` -- `NET_FILE` — path to SUMO route/network file (overridable via `NET_FILE` env var; default: `sumo/Repaired.rou.xml`) +- `NET_FILE` — path to SUMO route/network file (overridable via `NET_FILE` env var; default: `sumo/Repaired.net.xml`) - `DESTINATION_LIBRARY` / `ROUTE_LIBRARY` — hardcoded choice menus for agents - `OPENAI_MODEL` / `DECISION_PERIOD_S` — overridable via env vars diff --git a/agentevac/agents/agent_state.py b/agentevac/agents/agent_state.py index 0688090..ff7d92b 100644 --- a/agentevac/agents/agent_state.py +++ b/agentevac/agents/agent_state.py @@ -30,7 +30,7 @@ import math import random from dataclasses import dataclass, field -from typing import Any, Dict, List, Tuple +from typing import Any, Dict, List, Optional, Tuple @dataclass @@ -66,6 +66,10 @@ class AgentRuntimeState: decision_history: List[Dict[str, Any]] = field(default_factory=list) observation_history: List[Dict[str, Any]] = field(default_factory=list) has_departed: bool = True + last_input_hash: Optional[int] = None + last_llm_choice_idx: Optional[int] = None + last_llm_reason: Optional[str] = None + last_llm_action: Optional[str] = None # Global registry of all agent states, keyed by vehicle ID. diff --git a/agentevac/agents/routing_utility.py b/agentevac/agents/routing_utility.py index dc8ceb3..1f581f9 100644 --- a/agentevac/agents/routing_utility.py +++ b/agentevac/agents/routing_utility.py @@ -12,7 +12,7 @@ - ``E(option)`` : expected exposure score (``_expected_exposure``). - ``C(option)`` : travel cost in equivalent minutes (``_travel_cost``). -Expected exposure combines four components: +Expected exposure combines four components (in ``alert_guided`` and ``advice_guided``): 1. **risk_sum** : Sum of edge-level fire risk scores along the route (or fastest path). Scaled by a severity multiplier that increases with ``p_risky`` and ``p_danger``. 2. **blocked_edges** : Number of edges along the route that are currently inside a @@ -23,8 +23,14 @@ 4. **uncertainty_penalty** : A penalty proportional to ``(1 - confidence)`` that discourages fragile choices when the agent is unsure of the hazard. -Annotated menus (``annotate_menu_with_expected_utility``) are used in the *advice_guided* -scenario so the LLM receives pre-computed utility context alongside each option. +In ``no_notice`` mode, agents lack route-specific fire data. Exposure is instead +estimated from the agent's general belief state scaled by route length — longer routes +mean more time exposed to whatever danger the agent perceives. + +Annotated menus (``annotate_menu_with_expected_utility``) are computed for all three +scenarios so the LLM always receives a utility score. The *precision* of the exposure +estimate varies by information regime: belief-only (no_notice), current fire state +(alert_guided), or current fire state with full route-head data (advice_guided). """ from typing import Any, Dict, List @@ -107,6 +113,51 @@ def _travel_cost(menu_item: Dict[str, Any]) -> float: return _num(edge_count, 0.0) * 0.25 +def _observation_based_exposure( + menu_item: Dict[str, Any], + belief: Dict[str, Any], + psychology: Dict[str, Any], +) -> float: + """Estimate hazard exposure when route-specific fire data is unavailable. + + Used in the ``no_notice`` scenario where agents have only their own noisy + observation of the current edge. Without per-route fire metrics (risk_sum, + blocked_edges, min_margin_m), exposure is derived from the agent's general + belief state scaled by route length: + + hazard_level = 0.3 * p_risky + 0.7 * p_danger + 0.4 * perceived_risk + length_factor = len_edges * 0.15 + exposure = hazard_level * length_factor + uncertainty_penalty + + Longer routes are penalised more because a longer route means more time + spent driving through a potentially hazardous environment. The coefficients + prioritise ``p_danger`` (0.7) over ``p_risky`` (0.3) to maintain consistency + with the severity weighting in ``_expected_exposure``. + + Args: + menu_item: A destination or route dict. + belief: The agent's current Bayesian belief dict. + psychology: The agent's current psychology dict (perceived_risk, confidence). + + Returns: + Expected exposure score (>= 0; higher = more hazardous). + """ + p_risky = _num(belief.get("p_risky"), 1.0 / 3.0) + p_danger = _num(belief.get("p_danger"), 1.0 / 3.0) + perceived_risk = _num(psychology.get("perceived_risk"), p_danger) + confidence = _num(psychology.get("confidence"), 0.0) + + hazard_level = 0.3 * p_risky + 0.7 * p_danger + 0.4 * perceived_risk + len_edges = _num( + menu_item.get("len_edges", menu_item.get("len_edges_fastest_path")), + 1.0, + ) + length_factor = len_edges * 0.15 + uncertainty_penalty = max(0.0, 1.0 - confidence) * 0.75 + + return hazard_level * length_factor + uncertainty_penalty + + def _expected_exposure( menu_item: Dict[str, Any], belief: Dict[str, Any], @@ -227,14 +278,19 @@ def annotate_menu_with_expected_utility( belief: Dict[str, Any], psychology: Dict[str, Any], profile: Dict[str, Any], + scenario: str = "advice_guided", ) -> List[Dict[str, Any]]: """Annotate each menu option in-place with its expected utility and component breakdown. For *destination* mode, unreachable options (``reachable=False``) receive ``expected_utility=None`` and a minimal component dict to signal their exclusion. - The annotated menu is later filtered by ``scenarios.filter_menu_for_scenario`` so - that utility scores are only visible to agents in the *advice_guided* regime. + The ``scenario`` parameter controls which exposure function is used: + + - ``"no_notice"``: ``_observation_based_exposure`` — uses only the agent's belief + state and route length (no route-specific fire data). + - ``"alert_guided"`` / ``"advice_guided"``: ``_expected_exposure`` — uses route- + specific fire metrics (risk_sum, blocked_edges, min_margin_m). Args: menu: List of destination or route dicts (mutated in-place). @@ -242,6 +298,8 @@ def annotate_menu_with_expected_utility( belief: The agent's current Bayesian belief dict. psychology: The agent's current psychology dict. profile: The agent's profile dict (supplies ``lambda_e``, ``lambda_t``). + scenario: Active information regime (``"no_notice"``, ``"alert_guided"``, + or ``"advice_guided"``). Controls which exposure function is used. Returns: The same ``menu`` list, with each item updated to include: @@ -249,6 +307,9 @@ def annotate_menu_with_expected_utility( - ``utility_components``: Dict with lambda_e, lambda_t, expected_exposure, travel_cost (and ``reachable=False`` if unreachable). """ + use_observation_exposure = str(scenario).strip().lower() == "no_notice" + exposure_fn = _observation_based_exposure if use_observation_exposure else _expected_exposure + for item in menu: if mode == "destination": if not item.get("reachable", False): @@ -260,18 +321,17 @@ def annotate_menu_with_expected_utility( "reachable": False, } continue - expected_exposure = _expected_exposure(item, belief, psychology) - travel_cost = _travel_cost(item) - utility = score_destination_utility(item, belief, psychology, profile) - else: - expected_exposure = _expected_exposure(item, belief, psychology) - travel_cost = _travel_cost(item) - utility = score_route_utility(item, belief, psychology, profile) + + lambda_e = max(0.0, _num(profile.get("lambda_e"), 1.0)) + lambda_t = max(0.0, _num(profile.get("lambda_t"), 0.1)) + expected_exposure = exposure_fn(item, belief, psychology) + travel_cost = _travel_cost(item) + utility = -((lambda_e * expected_exposure) + (lambda_t * travel_cost)) item["expected_utility"] = round(utility, 4) item["utility_components"] = { - "lambda_e": round(max(0.0, _num(profile.get("lambda_e"), 1.0)), 4), - "lambda_t": round(max(0.0, _num(profile.get("lambda_t"), 0.1)), 4), + "lambda_e": round(lambda_e, 4), + "lambda_t": round(lambda_t, 4), "expected_exposure": round(expected_exposure, 4), "travel_cost": round(travel_cost, 4), } diff --git a/agentevac/agents/scenarios.py b/agentevac/agents/scenarios.py index 735c067..a92cc62 100644 --- a/agentevac/agents/scenarios.py +++ b/agentevac/agents/scenarios.py @@ -5,8 +5,9 @@ **no_notice** — No official warning exists yet. Agents rely solely on their own noisy margin observations and natural-language - messages from neighbours. Menu items contain only minimal fields (name, - reachability). This represents the typical onset of a rapidly spreading wildfire + messages from neighbours. Menu items include route identity, travel time, and + an observation-based utility score (local road knowledge), but no fire-specific + risk metrics. This represents the typical onset of a rapidly spreading wildfire before emergency services have issued formal guidance. **alert_guided** — Official alerts broadcast general hazard information. @@ -68,7 +69,7 @@ def load_scenario_config(mode: str) -> Dict[str, Any]: "forecast_visible": False, "route_head_forecast_visible": False, "official_route_guidance_visible": False, - "expected_utility_visible": False, + "expected_utility_visible": True, "neighborhood_observation_visible": True, } if name == "alert_guided": @@ -81,7 +82,7 @@ def load_scenario_config(mode: str) -> Dict[str, Any]: "forecast_visible": True, "route_head_forecast_visible": False, "official_route_guidance_visible": False, - "expected_utility_visible": False, + "expected_utility_visible": True, "neighborhood_observation_visible": True, } return { @@ -194,22 +195,27 @@ def filter_menu_for_scenario( for item in menu: out = dict(item) if not cfg["official_route_guidance_visible"]: - # Remove advisory labels produced by the operator briefing logic. + # Remove advisory labels and authority source produced by the operator briefing logic. out.pop("advisory", None) out.pop("briefing", None) out.pop("reasons", None) - - if not cfg["expected_utility_visible"]: - # Remove pre-computed utility scores so agents cannot use them as a shortcut. - out.pop("expected_utility", None) - out.pop("utility_components", None) + out.pop("guidance_source", None) if cfg["mode"] == "no_notice": - # Reduce to the bare minimum an agent could reasonably know without warnings. + # Keep fields an agent could plausibly know from local familiarity: + # route identity, reachability, travel time/length (local knowledge), + # and observation-based utility scores. if control_mode == "destination": - keep_keys = {"idx", "name", "dest_edge", "reachable", "note"} + keep_keys = { + "idx", "name", "dest_edge", "reachable", "note", + "travel_time_s_fastest_path", "len_edges_fastest_path", + "expected_utility", "utility_components", + } else: - keep_keys = {"idx", "name", "len_edges"} + keep_keys = { + "idx", "name", "len_edges", + "expected_utility", "utility_components", + } out = {k: v for k, v in out.items() if k in keep_keys} prompt_menu.append(out) @@ -240,17 +246,14 @@ def scenario_prompt_suffix(mode: str) -> str: ) if cfg["mode"] == "alert_guided": return ( - "This is an alert-guided scenario: official alerts describe the fire, but they do not prescribe a route. " - # "Use forecast and hazard cues, but make your own navigation choice." - "but do not prescribe a specific route. Do NOT invent route guidance. Use the provided official alert content, " - "hazard and forecast cues (if provided), and local road conditions to choose when, where and how to evacuate." - + "This is an alert-guided scenario: official alerts describe the fire but do not prescribe a specific route. " + "Do NOT invent route guidance. Use the provided official alert content, " + "hazard and forecast cues, and local road conditions to decide when, where, and how to evacuate." ) return ( - "This is an advice-guided scenario: official alerts include route-oriented guidance. " - "You may use advisories, briefings, and expected utility as formal support. " - # "ADVICE-GUIDED scenario: officials issue an evacuation *order* (leave immediately) and include route-oriented guidance (may be high-level and may change)." - "Default to following designated routes/instructions unless they are blocked, unsafe " - "or extremely congested; if deviating, state why and pick the safest feasible alternative. Stay responsive to updates." - + "This is an advice-guided evacuation: the Emergency Operations Center has issued official route guidance for your area. " + "Follow routes marked advisory='Recommended' unless they are physically blocked or impassable. " + "If you must deviate from official guidance, state why and choose the safest feasible alternative. " + "Delayed departure or ignoring recommended routes increases your exposure to dangerous fire conditions. " + "Stay responsive to updated guidance as conditions change." ) diff --git a/agentevac/analysis/metrics.py b/agentevac/analysis/metrics.py index 91f7678..f29545a 100644 --- a/agentevac/analysis/metrics.py +++ b/agentevac/analysis/metrics.py @@ -137,6 +137,10 @@ def record_arrival(self, agent_id: str, sim_t_s: float) -> None: self._arrival_times[agent_id] = float(sim_t_s) self._last_seen_time[agent_id] = float(sim_t_s) + def arrived_count(self) -> int: + """Return the number of agents that have arrived at their destination.""" + return len(self._arrival_times) + def observe_active_vehicles(self, active_vehicle_ids: List[str], sim_t_s: float) -> None: """Update the active-vehicle set for live bookkeeping only. diff --git a/agentevac/simulation/main.py b/agentevac/simulation/main.py index e0204d9..016ec40 100644 --- a/agentevac/simulation/main.py +++ b/agentevac/simulation/main.py @@ -22,6 +22,7 @@ **Key environment variables (override defaults without CLI):** OPENAI_MODEL : LLM model ID (default: gpt-4o-mini). DECISION_PERIOD_S : Seconds between LLM decision rounds (default: 5.0). + SIM_END_TIME_S : Max simulation duration in seconds (default: 1200). RUN_MODE : record | replay. REPLAY_LOG_PATH : Path to the JSONL replay log. EVENTS_LOG_PATH : Base path for the event stream JSONL. @@ -97,6 +98,7 @@ from agentevac.utils.replay import RouteReplay # ---- OpenAI (LLM control) ---- +from concurrent.futures import ThreadPoolExecutor from openai import OpenAI from pydantic import BaseModel, Field, conint, create_model @@ -123,7 +125,7 @@ CONTROL_MODE = "destination" # Your SUMO net file used by the .sumocfg (needed for edge geometry) -NET_FILE = os.getenv("NET_FILE", "sumo/Repaired.rou.xml") # override via NET_FILE env var +NET_FILE = os.getenv("NET_FILE", "sumo/Repaired.net.xml") # override via NET_FILE env var # OpenAI model + decision cadence OPENAI_MODEL = os.getenv("OPENAI_MODEL", "gpt-4o-mini") @@ -271,6 +273,7 @@ def _parse_cli_args() -> argparse.Namespace: parser.add_argument("--delay-heavy-ratio", type=float, help="Max delay ratio for 'heavy delay'.") parser.add_argument("--recommended-min-margin-m", type=float, help="Min margin for advisory='Recommended'.") parser.add_argument("--caution-min-margin-m", type=float, help="Min margin for advisory='Use with caution'.") + parser.add_argument("--sim-end-time", type=float, help="Simulation end time in seconds (default: 1200).") return parser.parse_args() @@ -434,6 +437,9 @@ def _agent_profile(agent_id: str) -> Dict[str, float]: CAUTION_MIN_MARGIN_M = _float_from_env_or_cli( CLI_ARGS.caution_min_margin_m, "CAUTION_MIN_MARGIN_M", 100.0 ) +SIM_END_TIME_S = _float_from_env_or_cli( + CLI_ARGS.sim_end_time, "SIM_END_TIME_S", 1200.0 +) if not (0.0 <= MARGIN_VERY_CLOSE_M <= MARGIN_NEAR_M <= MARGIN_BUFFERED_M): sys.exit( @@ -521,16 +527,19 @@ def _agent_profile(agent_id: str) -> Dict[str, float]: FIRE_SOURCES = [ # {"id": "F0", "t0": 0.0, "x": 9000.0, "y": 9000.0, "r0": 3000.0, "growth_m_per_s": 0.20}, # {"id": "F0_1", "t0": 0.0, "x": 9000.0, "y": 27000.0, "r0": 3000.0, "growth_m_per_s": 0.20}, -{"id": "F0", "t0": 0.0, "x": 22000.0, "y": 9000.0, "r0": 3000.0, "growth_m_per_s": 0.20}, - {"id": "F0_1", "t0": 0.0, "x": 24000.0, "y": 6000.0, "r0": 3000.0, "growth_m_per_s": 0.20}, +{"id": "F0", "t0": 0.0, "x": 22000.0, "y": 9000.0, "r0": 3000.0, "growth_m_per_s": 0.02}, + {"id": "F0_1", "t0": 0.0, "x": 24000.0, "y": 6000.0, "r0": 3000.0, "growth_m_per_s": 0.02}, + + ] NEW_FIRE_EVENTS = [ # {"id": "F1", "t0": 100.0, "x": 5000.0, "y": 4500.0, "r0": 2000.0, "growth_m_per_s": 0.30}, # {"id": "F0_2", "t0": 50.0, "x": 15000.0, "y": 21000.0, "r0": 3000.0, "growth_m_per_s": 0.20}, # {"id": "F0_3", "t0": 75.0, "x": 15000.0, "y": 15000.0, "r0": 3000.0, "growth_m_per_s": 0.20}, - {"id": "F1", "t0": 25.0, "x": 20000.0, "y": 12000.0, "r0": 2000.0, "growth_m_per_s": 0.30}, - {"id": "F0_2", "t0": 30.0, "x": 18000.0, "y": 14000.0, "r0": 3000.0, "growth_m_per_s": 0.20}, - {"id": "F0_3", "t0": 45.0, "x": 15000.0, "y": 18000.0, "r0": 3000.0, "growth_m_per_s": 0.20}, + {"id": "F1_4", "t0": 90.0, "x": 20000.0, "y": 6000.0, "r0": 3000.0, "growth_m_per_s": 0.02}, + {"id": "F1", "t0": 150.0, "x": 20000.0, "y": 12000.0, "r0": 2000.0, "growth_m_per_s": 0.02}, + {"id": "F1_2", "t0": 210.0, "x": 18000.0, "y": 14000.0, "r0": 3000.0, "growth_m_per_s": 0.02}, + {"id": "F1_3", "t0": 270.0, "x": 15000.0, "y": 18000.0, "r0": 3000.0, "growth_m_per_s": 0.02}, ] @@ -1377,6 +1386,7 @@ def _run_parameter_payload() -> Dict[str, Any]: return { "run_mode": RUN_MODE, "scenario": SCENARIO_MODE, + "sim_end_time_s": SIM_END_TIME_S, "sumo_binary": SUMO_BINARY, "messaging_controls": { "enabled": MESSAGING_ENABLED, @@ -1552,6 +1562,7 @@ def _run_parameter_payload() -> Dict[str, Any]: total_speed = 0 client = OpenAI() # uses OPENAI_API_KEY +MAX_CONCURRENT_LLM = int(os.environ.get("MAX_CONCURRENT_LLM", "20")) veh_last_choice: Dict[str, int] = {} decision_round_counter = 0 agent_round_history: Dict[str, deque] = {} @@ -1967,8 +1978,9 @@ def build_driver_briefing( reasons.append(f"Route is {proximity_phrase}.") reasons.append(f"Expected pace: {delay_phrase}.") - briefing = f"{advisory}: route {passability}, {proximity_phrase}, {delay_phrase}." + briefing = f"Emergency management assessment — {advisory}: route is currently {passability}, {proximity_phrase}, {delay_phrase}." return { + "guidance_source": "Emergency Operations Center", "advisory": advisory, "briefing": briefing, "reasons": reasons, @@ -1978,6 +1990,31 @@ def build_driver_briefing( } +def _decision_input_hash( + edge: str, + belief: Dict[str, Any], + inbox_len: int, + margin_m: Optional[float], + menu_utilities: Optional[tuple] = None, +) -> int: + """Compute a hash of the key LLM decision inputs for cache-skip detection. + + Rounded values prevent false misses from floating-point noise while still + detecting meaningful state changes. + """ + key = ( + edge, + round(float(belief.get("p_danger", 0)), 2), + round(float(belief.get("p_safe", 0)), 2), + round(float(belief.get("p_risky", 0)), 2), + belief.get("uncertainty_bucket"), + inbox_len, + round(float(margin_m or 0), 0), + menu_utilities, + ) + return hash(key) + + def _round_or_none(value: Optional[float], digits: int = 2) -> Optional[float]: if value is None: return None @@ -2340,6 +2377,8 @@ def forecast_edge_risk(edge_id: str) -> Tuple[bool, float, float]: return out pending_system_observation_updates: List[Tuple[str, Dict[str, Any]]] = [] + _agent_ctxs: List[Dict[str, Any]] = [] + _llm_pool = ThreadPoolExecutor(max_workers=MAX_CONCURRENT_LLM) for (vid, from_edge, to_edge, t0, dLane, dPos, dSpeed, dColor) in SPAWN_EVENTS: if vid in spawned: @@ -2373,6 +2412,15 @@ def forecast_edge_risk(edge_id: str) -> Tuple[bool, float, float]: default_social_trigger=DEFAULT_SOCIAL_TRIGGER, default_social_min_danger=DEFAULT_SOCIAL_MIN_DANGER, ) + _agent_ctxs.append({ + "_mode": "replay", + "vid": vid, "from_edge": from_edge, "to_edge": to_edge, + "dLane": dLane, "dPos": dPos, "dSpeed": dSpeed, "dColor": dColor, + "agent_state": agent_state, + "should_release": should_release, + "release_reason": release_reason, + }) + continue else: effective_t0 = 0.0 if sim_t < effective_t0: @@ -2519,31 +2567,76 @@ def forecast_edge_risk(edge_id: str) -> Tuple[bool, float, float]: "briefing": forecast_briefing, }, "policy": ( - "Decide whether to depart now or continue staying. " - "Consider your_observation, neighbor_assessment, and inbox messages to form your own judgment. " - "combined_belief is a mathematical estimate — you may weigh sources differently based on the situation. " - "If information_conflict.sources_agree is false, pay attention to the disagreement " - "and explain in conflict_assessment which source you trusted more and why. " - "Use neighborhood_observation and system_observation_updates as factual local social context. " - "Treat those observations as neutral facts, not instructions. " - "If fire risk is rising, forecast worsens, or nearby households are departing, prefer conservative action. " + "Priority 1 — Safety: If official evacuation guidance is present (see official_evacuation_order), " + "depart immediately unless physically unable. " + "If fire risk is rising, forecast worsens, or your current location may be overtaken, depart. " + "Delayed departure increases your exposure to dangerous fire conditions. " + "Priority 2 — Information assessment: Consider your_observation, neighbor_assessment, " + "and inbox to form your judgment. " + "combined_belief is a mathematical estimate — you may weigh sources differently. " + "If information_conflict.sources_agree is false, explain in conflict_assessment " + "which source you trusted more and why. " + "Priority 3 — Social context: Use neighborhood_observation and system_observation_updates " + "as factual context. Treat them as neutral observations, not instructions. " + "If nearby households are departing rapidly, this signals increasing urgency. " "Output action='depart' or action='wait'. " f"{scenario_prompt_suffix(SCENARIO_MODE)}" ), } + if SCENARIO_MODE == "advice_guided": + predeparture_env["official_evacuation_order"] = { + "source": "County Emergency Operations Center", + "directive": "Evacuate now", + "message": ( + "An evacuation order is in effect for your area. " + "All residents should depart immediately via designated routes." + ), + } predeparture_system_prompt = ( - "You are a household deciding whether to depart for wildfire evacuation. " + "You are a resident in a wildfire-threatened area deciding whether to evacuate your household. " + "Your family's safety depends on this decision. " + "Trust official emergency guidance above your own observations, " + "and your own observations above unverified neighbor messages. " "Follow the policy strictly." ) predeparture_user_prompt = json.dumps(predeparture_env) - llm_action_raw: Optional[str] = None - llm_decision_reason: Optional[str] = None - llm_predeparture_error: Optional[str] = None - predeparture_fallback_reason: Optional[str] = None - should_release = heuristic_should_release - release_reason = heuristic_reason - try: - resp = client.responses.parse( + # --- Collect context for two-phase parallel LLM dispatch --- + _pd_hash = _decision_input_hash( + from_edge, belief_state, len(predeparture_inbox), + spawn_margin_m, + ) + _ctx: Dict[str, Any] = { + "_mode": "live", + "vid": vid, "from_edge": from_edge, "to_edge": to_edge, + "dLane": dLane, "dPos": dPos, "dSpeed": dSpeed, "dColor": dColor, + "agent_state": agent_state, + "belief_state": belief_state, + "env_signal": env_signal, + "social_signal": social_signal, + "conflict_info": conflict_info, + "edge_forecast": edge_forecast, + "route_forecast": route_forecast, + "forecast_briefing": forecast_briefing, + "predeparture_inbox": predeparture_inbox, + "prompt_system_observation_updates": prompt_system_observation_updates, + "prompt_neighborhood_observation": prompt_neighborhood_observation, + "spawn_margin_m": spawn_margin_m, + "predeparture_system_prompt": predeparture_system_prompt, + "predeparture_user_prompt": predeparture_user_prompt, + "predeparture_env": predeparture_env, + "pd_hash": _pd_hash, + "heuristic_should_release": heuristic_should_release, + "heuristic_reason": heuristic_reason, + } + if ( + agent_state.last_input_hash == _pd_hash + and agent_state.last_llm_action is not None + ): + _ctx["_cached"] = True + else: + _ctx["_cached"] = False + _ctx["_future"] = _llm_pool.submit( + client.responses.parse, model=OPENAI_MODEL, input=[ {"role": "system", "content": predeparture_system_prompt}, @@ -2551,69 +2644,135 @@ def forecast_edge_risk(edge_id: str) -> Tuple[bool, float, float]: ], text_format=PreDepartureDecisionModel, ) - predeparture_decision = resp.output_parsed - llm_action_raw = str(getattr(predeparture_decision, "action", "") or "").strip().lower() - llm_decision_reason = getattr(predeparture_decision, "reason", None) + _agent_ctxs.append(_ctx) + continue # defer processing to Phase 2 below + + # ---- Phase 2: Wait for all LLM futures, then process results ---- + _llm_pool.shutdown(wait=True) + + for _ctx in _agent_ctxs: + vid = _ctx["vid"] + from_edge = _ctx["from_edge"] + to_edge = _ctx["to_edge"] + dLane = _ctx["dLane"] + dPos = _ctx["dPos"] + dSpeed = _ctx["dSpeed"] + dColor = _ctx["dColor"] + agent_state = _ctx["agent_state"] + + if _ctx["_mode"] == "replay": + should_release = _ctx["should_release"] + release_reason = _ctx["release_reason"] + else: + # Record/live mode: process LLM result + belief_state = _ctx["belief_state"] + env_signal = _ctx["env_signal"] + social_signal = _ctx["social_signal"] + predeparture_system_prompt = _ctx["predeparture_system_prompt"] + predeparture_user_prompt = _ctx["predeparture_user_prompt"] + heuristic_reason = _ctx["heuristic_reason"] + predeparture_inbox = _ctx["predeparture_inbox"] + prompt_system_observation_updates = _ctx["prompt_system_observation_updates"] + prompt_neighborhood_observation = _ctx["prompt_neighborhood_observation"] + edge_forecast = _ctx["edge_forecast"] + route_forecast = _ctx["route_forecast"] + forecast_briefing = _ctx["forecast_briefing"] + conflict_info = _ctx["conflict_info"] + + llm_action_raw: Optional[str] = None + llm_decision_reason: Optional[str] = None + llm_predeparture_error: Optional[str] = None + predeparture_fallback_reason: Optional[str] = None + should_release = _ctx["heuristic_should_release"] + release_reason = _ctx["heuristic_reason"] + + if _ctx["_cached"]: + llm_action_raw = agent_state.last_llm_action + llm_decision_reason = agent_state.last_llm_reason if llm_action_raw in {"depart", "leave", "depart_now"}: should_release = True - release_reason = "llm_depart" - elif llm_action_raw in {"wait", "stay", "hold"}: - should_release = False - release_reason = "llm_wait" + release_reason = "llm_depart_cached" else: - raise ValueError(f"Unsupported predeparture action: {llm_action_raw!r}") - llm_conflict_assessment = getattr(predeparture_decision, "conflict_assessment", None) - if EVENTS_ENABLED: - events.emit( - "predeparture_llm_decision", - summary=f"{vid} action={llm_action_raw}", - veh_id=vid, - action=llm_action_raw, - reason=llm_decision_reason, - conflict_assessment=llm_conflict_assessment, - round=decision_round_counter, - sim_t_s=sim_t, - ) + should_release = False + release_reason = "llm_wait_cached" replay.record_llm_dialog( - step=step_idx, - sim_t_s=sim_t, - veh_id=vid, - control_mode="predeparture", - model=OPENAI_MODEL, + step=step_idx, sim_t_s=sim_t, veh_id=vid, + control_mode="predeparture", model=OPENAI_MODEL, system_prompt=predeparture_system_prompt, user_prompt=predeparture_user_prompt, - response_text=getattr(resp, "output_text", None), - parsed=predeparture_decision.model_dump() - if hasattr(predeparture_decision, "model_dump") - else None, - error=None, + response_text=f"[cached] action={llm_action_raw}", + parsed=None, error=None, ) - except Exception as e: - llm_predeparture_error = str(e) - predeparture_fallback_reason = "heuristic_predeparture_fallback" - should_release = heuristic_should_release - release_reason = heuristic_reason - if EVENTS_ENABLED: - events.emit( - "predeparture_llm_error", - summary=f"{vid} error={e}", + else: + try: + resp = _ctx["_future"].result(timeout=60) + predeparture_decision = resp.output_parsed + llm_action_raw = str(getattr(predeparture_decision, "action", "") or "").strip().lower() + llm_decision_reason = getattr(predeparture_decision, "reason", None) + if llm_action_raw in {"depart", "leave", "depart_now"}: + should_release = True + release_reason = "llm_depart" + elif llm_action_raw in {"wait", "stay", "hold"}: + should_release = False + release_reason = "llm_wait" + else: + raise ValueError(f"Unsupported predeparture action: {llm_action_raw!r}") + llm_conflict_assessment = getattr(predeparture_decision, "conflict_assessment", None) + if EVENTS_ENABLED: + events.emit( + "predeparture_llm_decision", + summary=f"{vid} action={llm_action_raw}", + veh_id=vid, + action=llm_action_raw, + reason=llm_decision_reason, + conflict_assessment=llm_conflict_assessment, + round=decision_round_counter, + sim_t_s=sim_t, + ) + replay.record_llm_dialog( + step=step_idx, + sim_t_s=sim_t, veh_id=vid, - error=str(e), - round=decision_round_counter, + control_mode="predeparture", + model=OPENAI_MODEL, + system_prompt=predeparture_system_prompt, + user_prompt=predeparture_user_prompt, + response_text=getattr(resp, "output_text", None), + parsed=predeparture_decision.model_dump() + if hasattr(predeparture_decision, "model_dump") + else None, + error=None, + ) + except Exception as e: + llm_predeparture_error = str(e) + predeparture_fallback_reason = "heuristic_predeparture_fallback" + should_release = _ctx["heuristic_should_release"] + release_reason = _ctx["heuristic_reason"] + if EVENTS_ENABLED: + events.emit( + "predeparture_llm_error", + summary=f"{vid} error={e}", + veh_id=vid, + error=str(e), + round=decision_round_counter, + sim_t_s=sim_t, + ) + replay.record_llm_dialog( + step=step_idx, sim_t_s=sim_t, + veh_id=vid, + control_mode="predeparture", + model=OPENAI_MODEL, + system_prompt=predeparture_system_prompt, + user_prompt=predeparture_user_prompt, + response_text=None, + parsed=None, + error=str(e), ) - replay.record_llm_dialog( - step=step_idx, - sim_t_s=sim_t, - veh_id=vid, - control_mode="predeparture", - model=OPENAI_MODEL, - system_prompt=predeparture_system_prompt, - user_prompt=predeparture_user_prompt, - response_text=None, - parsed=None, - error=str(e), - ) + agent_state.last_input_hash = _ctx["pd_hash"] + agent_state.last_llm_action = llm_action_raw + agent_state.last_llm_reason = llm_decision_reason + replay.record_agent_cognition( step=step_idx, sim_t_s=sim_t, @@ -3255,20 +3414,32 @@ def record_agent_memory( belief=belief_state, psychology=agent_state.psychology, profile=agent_state.profile, + scenario=SCENARIO_MODE, ) prompt_destination_menu = filter_menu_for_scenario( SCENARIO_MODE, menu, control_mode="destination", ) - utility_policy = ( - "Use expected_utility as the main safety-efficiency tradeoff score; higher is better. " - if SCENARIO_CONFIG["expected_utility_visible"] - else "Do not assume a precomputed utility score is available in this scenario. " - ) + _utility_basis = { + "no_notice": ( + "expected_utility is available for all options; higher (less negative) is better. " + "Scores reflect your general hazard perception and route length — " + "you have no route-specific fire data. " + ), + "alert_guided": ( + "expected_utility is available for all options; higher (less negative) is better. " + "Scores incorporate current fire positions along each route. " + ), + "advice_guided": ( + "Use expected_utility as the main safety-efficiency tradeoff score; higher is better. " + ), + } + utility_policy = _utility_basis.get(SCENARIO_MODE, _utility_basis["advice_guided"]) guidance_policy = ( - "Prefer options with advisory='Recommended' and clear briefing reasons. " - "If advisory is not available, prefer lower risk_sum and larger min_margin. " + "The Emergency Operations Center has assessed each option. " + "Follow options with advisory='Recommended'; fall back to 'Use with caution' only if no recommended option is reachable. " + "Avoid options marked 'Avoid for now' unless all alternatives are blocked. " if SCENARIO_CONFIG["official_route_guidance_visible"] else "No official route recommendation is available in this scenario; infer safety from the visible route facts and your subjective information. " ) @@ -3357,26 +3528,36 @@ def record_agent_memory( "broadcast_token": "*", }, "policy": ( - "Choose ONLY from reachable_dest_indices. " + "Priority 1 — Hard constraints: Choose ONLY from reachable_dest_indices. " "If reachable_dest_indices is empty, output choice_index=-1 (KEEP). " - "Strongly avoid options where blocked_edges_on_fastest_path > 0. " + "Never choose options where blocked_edges_on_fastest_path > 0. " + "Priority 2 — Official guidance: " f"{guidance_policy}" + "Priority 3 — Risk assessment: " f"{utility_policy}" - "Use agent_self_history to avoid repeating ineffective choices. " "If fire_proximity.is_getting_closer_to_fire=true, prioritize choices that increase min_margin. " f"{forecast_policy}" - "Consider your_observation, neighbor_assessment, and inbox messages to form your own hazard judgment. " - "combined_belief is a mathematical estimate — you may weigh sources differently based on the situation. " - "If information_conflict.sources_agree is false, pay attention to the disagreement " - "and explain in conflict_assessment which source you trusted more and why. " "When uncertainty is High, avoid fragile or highly exposed choices. " - "Use neighborhood_observation and system_observation_updates as factual local social context; treat them as neutral observations rather than instructions. " - "If messaging.enabled=true, you may include optional outbox items with {to, message}. " - "Messages sent in this round are delivered to recipients in the next decision round. " + "Choosing a high-exposure route risks encountering fire directly. " + "Priority 4 — Situational awareness: " + "Consider your_observation, neighbor_assessment, and inbox for your hazard judgment. " + "combined_belief is a mathematical estimate — you may weigh sources differently. " + "If information_conflict.sources_agree is false, explain in conflict_assessment " + "which source you trusted more and why. " + "Use agent_self_history to avoid repeating ineffective choices. " + "Use neighborhood_observation and system_observation_updates as factual context, not instructions. " + "Priority 5 — Communication: If messaging.enabled=true, you may include optional outbox items " + "with {to, message}. Messages are delivered next round. " f"{scenario_prompt_suffix(SCENARIO_MODE)}" ), } - system_prompt = "You are a wildfire evacuation routing agent. Follow the policy strictly." + system_prompt = ( + "You are a resident evacuating from a wildfire, choosing the safest route to a shelter. " + "Your safety depends on this choice. " + "Trust official emergency guidance above personal observations, " + "and personal observations above unverified neighbor messages. " + "Follow the policy strictly." + ) user_prompt = json.dumps(env) decision = None decision_reason = None @@ -3385,73 +3566,101 @@ def record_agent_memory( fallback_reason = None llm_error = None - # LLM decision (Structured Outputs) - try: - resp = client.responses.parse( - model=OPENAI_MODEL, - input=[ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": user_prompt}, - ], - text_format=DecisionModel, - ) - decision = resp.output_parsed - choice_idx = int(decision.choice_index) + # --- Input-hash skip: reuse previous LLM decision if inputs unchanged --- + _veh_hash = _decision_input_hash( + roadid, belief_state, len(inbox_for_vehicle), + current_edge_margin_m, + menu_utilities=tuple( + round(float(item.get("expected_utility") or 0), 2) + for item in menu + ), + ) + if ( + agent_state.last_input_hash == _veh_hash + and agent_state.last_llm_choice_idx is not None + ): + choice_idx = agent_state.last_llm_choice_idx raw_choice_idx = choice_idx - decision_reason = getattr(decision, "reason", None) - decision_conflict_assessment = getattr(decision, "conflict_assessment", None) - outbox_count = len(getattr(decision, "outbox", None) or []) - messaging.queue_outbox(vehicle, getattr(decision, "outbox", None)) - if EVENTS_ENABLED: - events.emit( - "llm_decision", - summary=f"{vehicle} choice={choice_idx} outbox={outbox_count}", - veh_id=vehicle, - choice_idx=choice_idx, - reason=decision_reason, - conflict_assessment=decision_conflict_assessment, - outbox_count=outbox_count, - round=decision_round, - sim_t_s=sim_t_s, - ) + decision_reason = agent_state.last_llm_reason + fallback_reason = "cached" replay.record_llm_dialog( - step=step_idx, - sim_t_s=sim_t_s, - veh_id=vehicle, - control_mode=CONTROL_MODE, - model=OPENAI_MODEL, - system_prompt=system_prompt, - user_prompt=user_prompt, - response_text=getattr(resp, "output_text", None), - parsed=decision.model_dump() if hasattr(decision, "model_dump") else None, - error=None, + step=step_idx, sim_t_s=sim_t_s, veh_id=vehicle, + control_mode=CONTROL_MODE, model=OPENAI_MODEL, + system_prompt=system_prompt, user_prompt=user_prompt, + response_text=f"[cached] choice_index={choice_idx}", + parsed=None, error=None, ) - except Exception as e: - print(f"[WARN] LLM decision failed for {vehicle}: {e}") - llm_error = str(e) - fallback_reason = "llm_error" - if EVENTS_ENABLED: - events.emit( - "llm_error", - summary=f"{vehicle} error={e}", + else: + # LLM decision (Structured Outputs) + try: + resp = client.responses.parse( + model=OPENAI_MODEL, + input=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ], + text_format=DecisionModel, + ) + decision = resp.output_parsed + choice_idx = int(decision.choice_index) + raw_choice_idx = choice_idx + decision_reason = getattr(decision, "reason", None) + decision_conflict_assessment = getattr(decision, "conflict_assessment", None) + outbox_count = len(getattr(decision, "outbox", None) or []) + messaging.queue_outbox(vehicle, getattr(decision, "outbox", None)) + if EVENTS_ENABLED: + events.emit( + "llm_decision", + summary=f"{vehicle} choice={choice_idx} outbox={outbox_count}", + veh_id=vehicle, + choice_idx=choice_idx, + reason=decision_reason, + conflict_assessment=decision_conflict_assessment, + outbox_count=outbox_count, + round=decision_round, + sim_t_s=sim_t_s, + ) + replay.record_llm_dialog( + step=step_idx, + sim_t_s=sim_t_s, veh_id=vehicle, - error=str(e), - round=decision_round, + control_mode=CONTROL_MODE, + model=OPENAI_MODEL, + system_prompt=system_prompt, + user_prompt=user_prompt, + response_text=getattr(resp, "output_text", None), + parsed=decision.model_dump() if hasattr(decision, "model_dump") else None, + error=None, + ) + except Exception as e: + print(f"[WARN] LLM decision failed for {vehicle}: {e}") + llm_error = str(e) + fallback_reason = "llm_error" + if EVENTS_ENABLED: + events.emit( + "llm_error", + summary=f"{vehicle} error={e}", + veh_id=vehicle, + error=str(e), + round=decision_round, + sim_t_s=sim_t_s, + ) + replay.record_llm_dialog( + step=step_idx, sim_t_s=sim_t_s, + veh_id=vehicle, + control_mode=CONTROL_MODE, + model=OPENAI_MODEL, + system_prompt=system_prompt, + user_prompt=user_prompt, + response_text=None, + parsed=None, + error=str(e), ) - replay.record_llm_dialog( - step=step_idx, - sim_t_s=sim_t_s, - veh_id=vehicle, - control_mode=CONTROL_MODE, - model=OPENAI_MODEL, - system_prompt=system_prompt, - user_prompt=user_prompt, - response_text=None, - parsed=None, - error=str(e), - ) - choice_idx = -2 # trigger fallback + choice_idx = -2 # trigger fallback + agent_state.last_input_hash = _veh_hash + agent_state.last_llm_choice_idx = choice_idx + agent_state.last_llm_reason = decision_reason # Handle KEEP if choice_idx == -1: @@ -3648,19 +3857,32 @@ def record_agent_memory( belief=belief_state, psychology=agent_state.psychology, profile=agent_state.profile, + scenario=SCENARIO_MODE, ) prompt_route_menu = filter_menu_for_scenario( SCENARIO_MODE, menu, control_mode="route", ) - utility_policy = ( - "Use expected_utility as the main safety-efficiency tradeoff score; higher is better. " - if SCENARIO_CONFIG["expected_utility_visible"] - else "Do not assume a precomputed utility score is available in this scenario. " - ) + _rt_utility_basis = { + "no_notice": ( + "expected_utility is available for all options; higher (less negative) is better. " + "Scores reflect your general hazard perception and route length — " + "you have no route-specific fire data. " + ), + "alert_guided": ( + "expected_utility is available for all options; higher (less negative) is better. " + "Scores incorporate current fire positions along each route. " + ), + "advice_guided": ( + "Use expected_utility as the main safety-efficiency tradeoff score; higher is better. " + ), + } + utility_policy = _rt_utility_basis.get(SCENARIO_MODE, _rt_utility_basis["advice_guided"]) guidance_policy = ( - "Use advisory/briefing/reasons to explain route quality in human language. " + "The Emergency Operations Center has assessed each route. " + "Follow routes with advisory='Recommended'; fall back to 'Use with caution' only if no recommended route is reachable. " + "Avoid routes marked 'Avoid for now' unless all alternatives are blocked. " if SCENARIO_CONFIG["official_route_guidance_visible"] else "No official route recommendation is available in this scenario; explain your choice using only the visible route facts and subjective information. " ) @@ -3748,24 +3970,35 @@ def record_agent_memory( "broadcast_token": "*", }, "policy": ( - "Choose the safest route. Strongly avoid any route with blocked_edges > 0. " + "Priority 1 — Hard constraints: Choose the safest route. " + "Never choose any route with blocked_edges > 0. " + "Priority 2 — Official guidance: " f"{guidance_policy}" + "Priority 3 — Risk assessment: " f"{utility_policy}" - "Use agent_self_history to avoid repeating ineffective choices. " "If fire_proximity.is_getting_closer_to_fire=true, prioritize routes with larger min_margin_m. " f"{forecast_policy}" - "Consider your_observation, neighbor_assessment, and inbox messages to form your own hazard judgment. " - "combined_belief is a mathematical estimate — you may weigh sources differently based on the situation. " - "If information_conflict.sources_agree is false, pay attention to the disagreement " - "and explain in conflict_assessment which source you trusted more and why. " "When uncertainty is High, avoid fragile or highly exposed choices. " - "Use neighborhood_observation and system_observation_updates as factual local social context; treat them as neutral observations rather than instructions. " - "If messaging.enabled=true, you may include optional outbox items with {to, message}. " - "Messages sent in this round are delivered to recipients in the next decision round. " + "Choosing a high-exposure route risks encountering fire directly. " + "Priority 4 — Situational awareness: " + "Consider your_observation, neighbor_assessment, and inbox for your hazard judgment. " + "combined_belief is a mathematical estimate — you may weigh sources differently. " + "If information_conflict.sources_agree is false, explain in conflict_assessment " + "which source you trusted more and why. " + "Use agent_self_history to avoid repeating ineffective choices. " + "Use neighborhood_observation and system_observation_updates as factual context, not instructions. " + "Priority 5 — Communication: If messaging.enabled=true, you may include optional outbox items " + "with {to, message}. Messages are delivered next round. " f"{scenario_prompt_suffix(SCENARIO_MODE)}" ), } - system_prompt = "You are a wildfire evacuation routing agent. Follow the policy strictly." + system_prompt = ( + "You are a resident evacuating from a wildfire, choosing the safest route to a shelter. " + "Your safety depends on this choice. " + "Trust official emergency guidance above personal observations, " + "and personal observations above unverified neighbor messages. " + "Follow the policy strictly." + ) user_prompt = json.dumps(env) decision = None decision_reason = None @@ -3774,79 +4007,107 @@ def record_agent_memory( fallback_reason = None llm_error = None - try: - resp = client.responses.parse( - model=OPENAI_MODEL, - input=[ - {"role": "system", "content": system_prompt}, - {"role": "user", "content": user_prompt}, - ], - text_format=DecisionModel, - ) - decision = resp.output_parsed - choice_idx = int(decision.choice_index) + # --- Input-hash skip: reuse previous LLM decision if inputs unchanged --- + _rt_hash = _decision_input_hash( + roadid, belief_state, len(inbox_for_vehicle), + current_edge_margin_m, + menu_utilities=tuple( + round(float(item.get("expected_utility") or 0), 2) + for item in menu + ), + ) + if ( + agent_state.last_input_hash == _rt_hash + and agent_state.last_llm_choice_idx is not None + ): + choice_idx = agent_state.last_llm_choice_idx raw_choice_idx = choice_idx - decision_reason = getattr(decision, "reason", None) - decision_conflict_assessment = getattr(decision, "conflict_assessment", None) - outbox_count = len(getattr(decision, "outbox", None) or []) - messaging.queue_outbox(vehicle, getattr(decision, "outbox", None)) - if EVENTS_ENABLED: - events.emit( - "llm_decision", - summary=f"{vehicle} choice={choice_idx} outbox={outbox_count}", - veh_id=vehicle, - choice_idx=choice_idx, - reason=decision_reason, - conflict_assessment=decision_conflict_assessment, - outbox_count=outbox_count, - round=decision_round, - sim_t_s=sim_t_s, - ) + decision_reason = agent_state.last_llm_reason + fallback_reason = "cached" replay.record_llm_dialog( - step=step_idx, - sim_t_s=sim_t_s, - veh_id=vehicle, - control_mode=CONTROL_MODE, - model=OPENAI_MODEL, - system_prompt=system_prompt, - user_prompt=user_prompt, - response_text=getattr(resp, "output_text", None), - parsed=decision.model_dump() if hasattr(decision, "model_dump") else None, - error=None, + step=step_idx, sim_t_s=sim_t_s, veh_id=vehicle, + control_mode=CONTROL_MODE, model=OPENAI_MODEL, + system_prompt=system_prompt, user_prompt=user_prompt, + response_text=f"[cached] choice_index={choice_idx}", + parsed=None, error=None, ) - except Exception as e: - print(f"[WARN] LLM decision failed for {vehicle}: {e}") - llm_error = str(e) - fallback_reason = "llm_error" - if EVENTS_ENABLED: - events.emit( - "llm_error", - summary=f"{vehicle} error={e}", - veh_id=vehicle, - error=str(e), - round=decision_round, + else: + try: + resp = client.responses.parse( + model=OPENAI_MODEL, + input=[ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": user_prompt}, + ], + text_format=DecisionModel, + ) + decision = resp.output_parsed + choice_idx = int(decision.choice_index) + raw_choice_idx = choice_idx + decision_reason = getattr(decision, "reason", None) + decision_conflict_assessment = getattr(decision, "conflict_assessment", None) + outbox_count = len(getattr(decision, "outbox", None) or []) + messaging.queue_outbox(vehicle, getattr(decision, "outbox", None)) + if EVENTS_ENABLED: + events.emit( + "llm_decision", + summary=f"{vehicle} choice={choice_idx} outbox={outbox_count}", + veh_id=vehicle, + choice_idx=choice_idx, + reason=decision_reason, + conflict_assessment=decision_conflict_assessment, + outbox_count=outbox_count, + round=decision_round, + sim_t_s=sim_t_s, + ) + replay.record_llm_dialog( + step=step_idx, sim_t_s=sim_t_s, + veh_id=vehicle, + control_mode=CONTROL_MODE, + model=OPENAI_MODEL, + system_prompt=system_prompt, + user_prompt=user_prompt, + response_text=getattr(resp, "output_text", None), + parsed=decision.model_dump() if hasattr(decision, "model_dump") else None, + error=None, ) - replay.record_llm_dialog( - step=step_idx, - sim_t_s=sim_t_s, - veh_id=vehicle, - control_mode=CONTROL_MODE, - model=OPENAI_MODEL, - system_prompt=system_prompt, - user_prompt=user_prompt, - response_text=None, - parsed=None, - error=str(e), - ) - choice_idx = sorted( - range(len(menu)), - key=lambda i: ( - -float(menu[i].get("expected_utility", -10**9)), - menu[i]["blocked_edges"], - menu[i]["risk_sum"], + except Exception as e: + print(f"[WARN] LLM decision failed for {vehicle}: {e}") + llm_error = str(e) + fallback_reason = "llm_error" + if EVENTS_ENABLED: + events.emit( + "llm_error", + summary=f"{vehicle} error={e}", + veh_id=vehicle, + error=str(e), + round=decision_round, + sim_t_s=sim_t_s, + ) + replay.record_llm_dialog( + step=step_idx, + sim_t_s=sim_t_s, + veh_id=vehicle, + control_mode=CONTROL_MODE, + model=OPENAI_MODEL, + system_prompt=system_prompt, + user_prompt=user_prompt, + response_text=None, + parsed=None, + error=str(e), ) - )[0] + choice_idx = sorted( + range(len(menu)), + key=lambda i: ( + -float(menu[i].get("expected_utility", -10**9)), + menu[i]["blocked_edges"], + menu[i]["risk_sum"], + ) + )[0] + agent_state.last_input_hash = _rt_hash + agent_state.last_llm_choice_idx = choice_idx + agent_state.last_llm_reason = decision_reason selected_item = next((x for x in menu if x.get("idx") == choice_idx), None) if OVERLAYS_ENABLED: @@ -4076,11 +4337,12 @@ def update_fire_shapes(sim_t_s: float): # ========================= -# Step 8: Take simulation steps until there are no more vehicles in the network +# Step 8: Take simulation steps until sim end time is reached # ========================= step_idx = 0 +print(f"[SIM] Simulation will run until t={SIM_END_TIME_S:.0f}s (--sim-end-time / SIM_END_TIME_S)") try: - while traci.simulation.getMinExpectedNumber() > 0: + while traci.simulation.getTime() < SIM_END_TIME_S: traci.simulationStep() step_idx += 1 # --- NEW: visualize fire spread each step (or each decision round if you prefer) --- @@ -4104,27 +4366,33 @@ def update_fire_shapes(sim_t_s: float): ) active_vehicle_ids = list(traci.vehicle.getIDList()) _refresh_active_agent_live_status(sim_t, active_vehicle_ids) - fires = active_fires(sim_t) - fire_geom = [(float(item["x"]), float(item["y"]), float(item["r"])) for item in fires] - for vid in active_vehicle_ids: - try: - roadid = traci.vehicle.getRoadID(vid) - if not roadid or roadid.startswith(":"): - continue - _, risk_score, margin_m = compute_edge_risk_for_fires(roadid, fire_geom) - metrics.record_exposure_sample( - agent_id=vid, - sim_t_s=sim_t, - current_edge=roadid, - current_margin_m=_round_or_none(margin_m, 2), - risk_score=risk_score, - ) - except traci.TraCIException: - continue metrics.observe_active_vehicles(active_vehicle_ids, sim_t) + # Early termination: stop when all agents arrived at their destination + if len(spawned) == len(SPAWN_EVENTS) and metrics.arrived_count() == len(SPAWN_EVENTS): + print(f"[SIM] All {len(SPAWN_EVENTS)} agents arrived at destination by t={sim_t:.1f}s — ending early.") + break delta_t = traci.simulation.getDeltaT() decision_period_steps = max(1, int(round(DECISION_PERIOD_S / max(1e-9, delta_t)))) if step_idx % decision_period_steps == 0: + # Record exposure once per decision round (not every step) to avoid + # diluting the average with many low-risk samples between rounds. + fires = active_fires(sim_t) + fire_geom = [(float(item["x"]), float(item["y"]), float(item["r"])) for item in fires] + for vid in active_vehicle_ids: + try: + roadid = traci.vehicle.getRoadID(vid) + if not roadid or roadid.startswith(":"): + continue + _, risk_score, margin_m = compute_edge_risk_for_fires(roadid, fire_geom) + metrics.record_exposure_sample( + agent_id=vid, + sim_t_s=sim_t, + current_edge=roadid, + current_margin_m=_round_or_none(margin_m, 2), + risk_score=risk_score, + ) + except traci.TraCIException: + continue replay.record_metric_snapshot( step=step_idx, sim_t_s=sim_t, diff --git a/scripts/run_rq1_info_quality.sh b/scripts/run_rq1_info_quality.sh new file mode 100755 index 0000000..f57992c --- /dev/null +++ b/scripts/run_rq1_info_quality.sh @@ -0,0 +1,50 @@ +#!/usr/bin/env bash +# ============================================================================== +# RQ1: Information Quality → Departure Timing, Route Choice, Decision Instability +# +# Research question: +# How do information quality factors (observation noise σ_info, information +# delay, and conflicting signals) affect departure timing, route-choice +# distribution, and decision instability? +# +# Design: +# IV1: INFO_SIGMA = {0, 20, 40, 80} (observation noise) +# IV2: INFO_DELAY_S = {0, 15, 30, 60} (information delay) +# Moderator: scenario = {no_notice, alert_guided, advice_guided} +# Messaging: on (fixed — required for signal conflict to emerge) +# theta_trust: 0.5 (fixed — social channel held neutral) +# Population: homogeneous (all spreads = 0) +# Seeds: 5 (stochastic replication) +# +# Grid: 4 sigma × 4 delay × 3 scenario × 5 seeds = 240 runs +# +# Primary DVs: +# - departure_time_variability +# - route_choice_entropy +# - decision_instability (average_changes, max_changes) +# - average_signal_conflict (JSD — measures conflicting signals) +# - destination_choice_share +# - departed_agents, arrived_agents +# ============================================================================== +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "$0")/.." && pwd)" +cd "$ROOT_DIR" + +SEEDS=(12345 12346 12347 12348 12349) + +for seed in "${SEEDS[@]}"; do + echo "============================================" + echo "[RQ1] seed=${seed}" + echo "============================================" + SUMO_SEED="$seed" python3 -m agentevac.analysis.experiments \ + --output-dir "outputs/rq1/info_quality_seed_${seed}" \ + --sumo-binary sumo \ + --sigma-values 0,20,40,80 \ + --delay-values 0,15,30,60 \ + --trust-values 0.5 \ + --scenario-values no_notice,alert_guided,advice_guided \ + --messaging on +done + +echo "[RQ1] All seeds complete." diff --git a/scripts/run_rq2_social_trust.sh b/scripts/run_rq2_social_trust.sh new file mode 100755 index 0000000..6f82895 --- /dev/null +++ b/scripts/run_rq2_social_trust.sh @@ -0,0 +1,59 @@ +#!/usr/bin/env bash +# ============================================================================== +# RQ2: Social Cues × Trust → Cascades, Herding, Belief Dynamics +# +# Research question: +# Given fixed information quality, how do social cues and trust (θ_trust) +# interact to shape collective departure cascades, herding in route/ +# destination choice, and changes in decision instability and belief +# uncertainty over time? +# +# Design: +# IV1: DEFAULT_THETA_TRUST = {0.0, 0.25, 0.5, 0.75, 1.0} +# IV2: messaging = {on, off} +# Moderator: scenario = {no_notice, alert_guided, advice_guided} +# INFO_SIGMA: 40 (fixed — moderate noise) +# INFO_DELAY_S: 30 (fixed — moderate delay) +# Population: homogeneous (all spreads = 0) +# Seeds: 5 (stochastic replication) +# +# Grid: 5 trust × 2 messaging × 3 scenario × 5 seeds = 150 runs +# +# Primary DVs (from metrics JSON): +# - departure_time_variability (cascade synchronization) +# - route_choice_entropy (herding) +# - decision_instability (flip-flopping) +# - average_signal_conflict (env vs. social disagreement) +# - destination_choice_share (which destinations attract herds) +# - departed_agents, arrived_agents +# +# Time-series DVs (post-process from replay JSONL): +# - belief entropy over time (agent_cognition events) +# - confidence over time (agent_cognition events) +# - departure reason counts (departure_release events) +# - cascade chain analysis (departure_release reason=neighbor_departure_activity) +# ============================================================================== +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "$0")/.." && pwd)" +cd "$ROOT_DIR" + +SEEDS=(12345 12346 12347 12348 12349) + +for messaging in on off; do + for seed in "${SEEDS[@]}"; do + echo "============================================" + echo "[RQ2] messaging=${messaging} seed=${seed}" + echo "============================================" + SUMO_SEED="$seed" python3 -m agentevac.analysis.experiments \ + --output-dir "outputs/rq2/social_trust_msg_${messaging}_seed_${seed}" \ + --sumo-binary sumo \ + --sigma-values 40 \ + --delay-values 30 \ + --trust-values 0.0,0.25,0.5,0.75,1.0 \ + --scenario-values no_notice,alert_guided,advice_guided \ + --messaging "$messaging" + done +done + +echo "[RQ2] All conditions complete." diff --git a/scripts/run_rq3_pareto.sh b/scripts/run_rq3_pareto.sh new file mode 100755 index 0000000..375e1c7 --- /dev/null +++ b/scripts/run_rq3_pareto.sh @@ -0,0 +1,57 @@ +#!/usr/bin/env bash +# ============================================================================== +# RQ3: Safety–Efficiency Pareto Frontier Across Alerting Regimes +# +# Research question: +# How does the safety-efficiency trade-off (average hazard exposure E_avg +# vs average travel time T_avg) change across combinations of information +# quality, delay, and trust? How does the resulting Pareto frontier shift +# under the three alerting regimes (no-notice, alert-guided, advice-guided)? +# +# Design: +# IV1: INFO_SIGMA = {20, 40, 80} +# IV2: INFO_DELAY_S = {0, 30, 60} +# IV3: DEFAULT_THETA_TRUST = {0.25, 0.5, 0.75} +# Grouping: scenario = {no_notice, alert_guided, advice_guided} +# Messaging: on (fixed) +# Population: homogeneous (all spreads = 0) +# Seeds: 5 (stochastic replication) +# +# Grid: 3 sigma × 3 delay × 3 trust × 3 scenario × 5 seeds = 405 runs +# +# Primary DVs: +# - average_hazard_exposure (E_avg — safety axis) +# - average_travel_time (T_avg — efficiency axis) +# - arrived_agents (completion filter) +# +# Per-agent DVs (for equity analysis): +# - per_agent hazard exposure +# - per_agent travel time +# +# Analysis-level metrics (computed post-hoc): +# - Pareto frontier per scenario +# - Hypervolume indicator per scenario +# - Frontier shift between scenarios +# ============================================================================== +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "$0")/.." && pwd)" +cd "$ROOT_DIR" + +SEEDS=(12345 12346 12347 12348 12349) + +for seed in "${SEEDS[@]}"; do + echo "============================================" + echo "[RQ3] seed=${seed}" + echo "============================================" + SUMO_SEED="$seed" python3 -m agentevac.analysis.experiments \ + --output-dir "outputs/rq3/pareto_seed_${seed}" \ + --sumo-binary sumo \ + --sigma-values 20,40,80 \ + --delay-values 0,30,60 \ + --trust-values 0.25,0.5,0.75 \ + --scenario-values no_notice,alert_guided,advice_guided \ + --messaging on +done + +echo "[RQ3] All seeds complete." diff --git a/scripts/run_rq4_heterogeneity.sh b/scripts/run_rq4_heterogeneity.sh new file mode 100755 index 0000000..a923262 --- /dev/null +++ b/scripts/run_rq4_heterogeneity.sh @@ -0,0 +1,83 @@ +#!/usr/bin/env bash +# ============================================================================== +# RQ4: Population Heterogeneity — Diversity as Resilience or Fragility +# +# Research question: +# Does heterogeneity in risk tolerance and decision weights improve +# system-level evacuation resilience, and does the optimal diversity +# level depend on the information regime? +# +# Design: +# IV1: spread_level = {none, low, moderate, high} +# Moderator: scenario = {no_notice, alert_guided, advice_guided} +# INFO_SIGMA: 40 (fixed — moderate noise) +# INFO_DELAY_S: 30 (fixed — moderate delay) +# theta_trust mean: 0.5 (fixed) +# Messaging: on (fixed — social channel needed for canary cascade) +# Seeds: 10 (more seeds for stochastic spread effects) +# +# Spread levels (std-dev of truncated normal around population means): +# ┌──────────────┬──────┬──────┬──────────┬──────┐ +# │ Parameter │ none │ low │ moderate │ high │ +# ├──────────────┼──────┼──────┼──────────┼──────┤ +# │ theta_trust │ 0.0 │ 0.05 │ 0.12 │ 0.20 │ +# │ theta_r │ 0.0 │ 0.03 │ 0.08 │ 0.15 │ +# │ theta_u │ 0.0 │ 0.03 │ 0.08 │ 0.15 │ +# │ gamma │ 0.0 │ 0.001│ 0.003 │ 0.005│ +# │ lambda_e │ 0.0 │ 0.15 │ 0.4 │ 0.8 │ +# │ lambda_t │ 0.0 │ 0.03 │ 0.08 │ 0.15 │ +# └──────────────┴──────┴──────┴──────────┴──────┘ +# +# Grid: 4 spread × 3 scenario × 10 seeds = 120 runs +# +# Primary DVs: +# - arrived_agents, average_hazard_exposure, average_travel_time +# - departure_time_variability, route_choice_entropy, decision_instability +# +# Per-agent DVs (for equity / Gini analysis): +# - per_agent hazard exposure, per_agent travel time +# +# Time-series DVs (post-process from replay JSONL): +# - departure reason distribution (canary cascade detection) +# ============================================================================== +set -euo pipefail + +ROOT_DIR="$(cd "$(dirname "$0")/.." && pwd)" +cd "$ROOT_DIR" + +SEEDS=(12345 12346 12347 12348 12349 12350 12351 12352 12353 12354) + +# Spread level definitions: (label, theta_trust, theta_r, theta_u, gamma, lambda_e, lambda_t) +SPREAD_LABELS=( "none" "low" "moderate" "high" ) +SPREAD_TRUST=( "0.0" "0.05" "0.12" "0.20" ) +SPREAD_TR=( "0.0" "0.03" "0.08" "0.15" ) +SPREAD_TU=( "0.0" "0.03" "0.08" "0.15" ) +SPREAD_GAMMA=( "0.0" "0.001" "0.003" "0.005") +SPREAD_LE=( "0.0" "0.15" "0.4" "0.8" ) +SPREAD_LT=( "0.0" "0.03" "0.08" "0.15" ) + +for i in "${!SPREAD_LABELS[@]}"; do + spread="${SPREAD_LABELS[$i]}" + for seed in "${SEEDS[@]}"; do + echo "============================================" + echo "[RQ4] spread=${spread} seed=${seed}" + echo "============================================" + SUMO_SEED="$seed" \ + THETA_TRUST_SPREAD="${SPREAD_TRUST[$i]}" \ + THETA_R_SPREAD="${SPREAD_TR[$i]}" \ + THETA_U_SPREAD="${SPREAD_TU[$i]}" \ + GAMMA_SPREAD="${SPREAD_GAMMA[$i]}" \ + LAMBDA_E_SPREAD="${SPREAD_LE[$i]}" \ + LAMBDA_T_SPREAD="${SPREAD_LT[$i]}" \ + python3 -m agentevac.analysis.experiments \ + --output-dir "outputs/rq4/heterogeneity_spread_${spread}_seed_${seed}" \ + --sumo-binary sumo \ + --sigma-values 40 \ + --delay-values 30 \ + --trust-values 0.5 \ + --scenario-values no_notice,alert_guided,advice_guided \ + --messaging on + done +done + +echo "[RQ4] All conditions complete." diff --git a/sumo/Repaired.net.xml b/sumo/Repaired.net.xml new file mode 100644 index 0000000..55e28ab --- /dev/null +++ b/sumo/Repaired.net.xml @@ -0,0 +1,7446 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sumo/Repaired.netecfg b/sumo/Repaired.netecfg index f9322ad..8978d25 100644 --- a/sumo/Repaired.netecfg +++ b/sumo/Repaired.netecfg @@ -1,6 +1,6 @@ - @@ -9,7 +9,7 @@ - + diff --git a/sumo/Repaired.rou.xml b/sumo/Repaired.rou.xml index 29df917..34d6f9c 100644 --- a/sumo/Repaired.rou.xml +++ b/sumo/Repaired.rou.xml @@ -1,9 +1,7 @@ - - - diff --git a/sumo/Repaired.sumocfg b/sumo/Repaired.sumocfg index 6f4785e..65c0438 100644 --- a/sumo/Repaired.sumocfg +++ b/sumo/Repaired.sumocfg @@ -1,12 +1,12 @@ - - + diff --git a/tests/test_routing_utility.py b/tests/test_routing_utility.py index 43b05a9..ecbdb96 100644 --- a/tests/test_routing_utility.py +++ b/tests/test_routing_utility.py @@ -3,6 +3,7 @@ import pytest from agentevac.agents.routing_utility import ( + _observation_based_exposure, annotate_menu_with_expected_utility, score_destination_utility, score_route_utility, @@ -172,3 +173,116 @@ def test_higher_risk_gets_lower_utility(self): psychology=_psychology(confidence=0.1), profile=_profile() ) assert menu[0]["expected_utility"] > menu[1]["expected_utility"] + + +class TestObservationBasedExposure: + """Tests for the no_notice exposure function that uses only belief + route length.""" + + def test_zero_danger_gives_low_exposure(self): + item = {"len_edges": 5} + belief = {"p_safe": 0.9, "p_risky": 0.05, "p_danger": 0.05} + psych = {"perceived_risk": 0.05, "confidence": 0.8} + exposure = _observation_based_exposure(item, belief, psych) + assert exposure < 1.0 + + def test_high_danger_gives_high_exposure(self): + item = {"len_edges": 5} + belief = {"p_safe": 0.05, "p_risky": 0.05, "p_danger": 0.9} + psych = {"perceived_risk": 0.8, "confidence": 0.1} + exposure = _observation_based_exposure(item, belief, psych) + assert exposure > 1.0 + + def test_longer_route_gives_more_exposure(self): + belief = {"p_safe": 0.1, "p_risky": 0.3, "p_danger": 0.6} + psych = {"perceived_risk": 0.5, "confidence": 0.5} + short = _observation_based_exposure({"len_edges": 3}, belief, psych) + long = _observation_based_exposure({"len_edges": 15}, belief, psych) + assert long > short + + def test_same_length_same_belief_gives_same_exposure(self): + belief = _neutral_belief() + psych = _psychology() + e1 = _observation_based_exposure({"len_edges": 5}, belief, psych) + e2 = _observation_based_exposure({"len_edges": 5}, belief, psych) + assert e1 == pytest.approx(e2) + + def test_low_confidence_adds_uncertainty_penalty(self): + item = {"len_edges": 5} + belief = _neutral_belief() + confident = _observation_based_exposure(item, belief, _psychology(confidence=0.9)) + uncertain = _observation_based_exposure(item, belief, _psychology(confidence=0.1)) + assert uncertain > confident + + def test_uses_len_edges_fastest_path_fallback(self): + belief = _neutral_belief() + psych = _psychology() + item_a = {"len_edges": 10} + item_b = {"len_edges_fastest_path": 10} + assert _observation_based_exposure(item_a, belief, psych) == pytest.approx( + _observation_based_exposure(item_b, belief, psych) + ) + + +class TestAnnotateMenuScenarioParam: + """Tests that the scenario parameter selects the correct exposure function.""" + + def _make_menu(self): + return [ + { + "idx": 0, "name": "s0", "reachable": True, + "risk_sum": 3.0, "blocked_edges": 1, "min_margin_m": 50.0, + "travel_time_s_fastest_path": 300.0, "len_edges_fastest_path": 8, + }, + ] + + def test_no_notice_uses_observation_based_exposure(self): + menu = self._make_menu() + annotate_menu_with_expected_utility( + menu, mode="destination", belief=_neutral_belief(), + psychology=_psychology(), profile=_profile(), scenario="no_notice", + ) + # Observation-based exposure ignores risk_sum and blocked_edges, + # so it should be much lower than route-specific exposure. + obs_exposure = menu[0]["utility_components"]["expected_exposure"] + + menu2 = self._make_menu() + annotate_menu_with_expected_utility( + menu2, mode="destination", belief=_neutral_belief(), + psychology=_psychology(), profile=_profile(), scenario="advice_guided", + ) + full_exposure = menu2[0]["utility_components"]["expected_exposure"] + # Route with blocked_edges=1 and risk_sum=3.0 should have much higher + # full exposure than belief-only exposure. + assert full_exposure > obs_exposure + + def test_advice_guided_uses_route_specific_exposure(self): + menu = self._make_menu() + annotate_menu_with_expected_utility( + menu, mode="destination", belief=_neutral_belief(), + psychology=_psychology(), profile=_profile(), scenario="advice_guided", + ) + # With blocked_edges=1, the exposure should include the 8.0 penalty. + assert menu[0]["utility_components"]["expected_exposure"] > 8.0 + + def test_alert_guided_uses_route_specific_exposure(self): + menu = self._make_menu() + annotate_menu_with_expected_utility( + menu, mode="destination", belief=_neutral_belief(), + psychology=_psychology(), profile=_profile(), scenario="alert_guided", + ) + assert menu[0]["utility_components"]["expected_exposure"] > 8.0 + + def test_default_scenario_is_advice_guided(self): + menu_default = self._make_menu() + menu_explicit = self._make_menu() + annotate_menu_with_expected_utility( + menu_default, mode="destination", belief=_neutral_belief(), + psychology=_psychology(), profile=_profile(), + ) + annotate_menu_with_expected_utility( + menu_explicit, mode="destination", belief=_neutral_belief(), + psychology=_psychology(), profile=_profile(), scenario="advice_guided", + ) + assert menu_default[0]["expected_utility"] == pytest.approx( + menu_explicit[0]["expected_utility"] + ) diff --git a/tests/test_scenarios.py b/tests/test_scenarios.py index d56123f..c7d63e4 100644 --- a/tests/test_scenarios.py +++ b/tests/test_scenarios.py @@ -16,14 +16,14 @@ def test_no_notice_mode(self): cfg = load_scenario_config("no_notice") assert cfg["mode"] == "no_notice" assert cfg["forecast_visible"] is False - assert cfg["expected_utility_visible"] is False + assert cfg["expected_utility_visible"] is True def test_alert_guided_mode(self): cfg = load_scenario_config("alert_guided") assert cfg["mode"] == "alert_guided" assert cfg["forecast_visible"] is True assert cfg["route_head_forecast_visible"] is False - assert cfg["expected_utility_visible"] is False + assert cfg["expected_utility_visible"] is True def test_advice_guided_mode(self): cfg = load_scenario_config("advice_guided") @@ -139,11 +139,11 @@ def test_alert_guided_removes_advisory(self): assert "briefing" not in result[0] assert "reasons" not in result[0] - def test_alert_guided_removes_expected_utility(self): + def test_alert_guided_retains_expected_utility(self): menu = self._full_menu() result = filter_menu_for_scenario("alert_guided", menu, control_mode="destination") - assert "expected_utility" not in result[0] - assert "utility_components" not in result[0] + assert "expected_utility" in result[0] + assert "utility_components" in result[0] def test_alert_guided_retains_risk_fields(self): menu = self._full_menu() @@ -151,18 +151,31 @@ def test_alert_guided_retains_risk_fields(self): assert "risk_sum" in result[0] assert "blocked_edges" in result[0] - def test_no_notice_destination_reduces_to_minimal_keys(self): + def test_no_notice_destination_keeps_local_knowledge_and_utility(self): menu = self._full_menu() + menu[0]["travel_time_s_fastest_path"] = 300.0 + menu[0]["len_edges_fastest_path"] = 8 result = filter_menu_for_scenario("no_notice", menu, control_mode="destination") - allowed = {"idx", "name", "dest_edge", "reachable", "note"} + allowed = { + "idx", "name", "dest_edge", "reachable", "note", + "travel_time_s_fastest_path", "len_edges_fastest_path", + "expected_utility", "utility_components", + } assert set(result[0].keys()).issubset(allowed) assert "risk_sum" not in result[0] - - def test_no_notice_route_mode_reduces_to_minimal_keys(self): - menu = [{"idx": 0, "name": "r0", "len_edges": 5, "risk_sum": 2.0}] + assert "expected_utility" in result[0] + assert "travel_time_s_fastest_path" in result[0] + + def test_no_notice_route_mode_keeps_utility_and_length(self): + menu = [{ + "idx": 0, "name": "r0", "len_edges": 5, "risk_sum": 2.0, + "expected_utility": -0.3, "utility_components": {"expected_exposure": 0.1}, + }] result = filter_menu_for_scenario("no_notice", menu, control_mode="route") assert "risk_sum" not in result[0] assert "len_edges" in result[0] + assert "expected_utility" in result[0] + assert "utility_components" in result[0] def test_original_menu_list_not_mutated(self): menu = self._full_menu()